class_docstr: A wrapper for torch.optim.Optimizer objects that helps with managing dynamically generated parameters.
  
  :param optim_constructor: a torch.optim.Optimizer
  :param optim_args: a dictionary of learning arguments for the optimizer or a callable that returns
      such dictionaries
  :param clip_args: a dictionary of clip_norm and/or clip_value args or a callable that returns
      such dictionaries
code: def _get_grad_clip(self, param: str):
      grad_clip_args = self._get_grad_clip_args(param)
  
      if not grad_clip_args:
          return None
  
      def _clip_grad(params: Union[Tensor, Iterable[Tensor]]):
          self._clip_grad(params, **grad_clip_args)
  
      return _clip_grad
  
imports: import inspect
  from typing import Any, Callable, Dict, Iterable, List, Optional, Type, Union, ValuesView
  import torch
  from torch import Tensor
  from torch.nn.utils import clip_grad_norm_, clip_grad_value_
  from torch.optim import Optimizer
  import pyro
  from pyro.optim.adagrad_rmsprop import AdagradRMSProp
  from pyro.optim.clipped_adam import ClippedAdam
  from pyro.optim.dct_adam import DCTAdam
  from pyro.params.param_store import module_from_param_with_module_name, normalize_param_name, user_param_name
label_desc: Use image node_id 9 for calling the PyroOptim obj's underlying member method code with example usage: obj._get_grad_clip(param) and returns: _clip_grad, None